@article {317, title = {Efficient FFT-accelerated approach to invariant optical-LIDAR registration}, journal = {IEEE Transactions on Geoscience and Remote Sensing}, volume = {46}, year = {2008}, pages = {3917 - 3925}, abstract = {

This paper presents a fast Fourier transform (FFT)-accelerated approach designed to handle many of the difficulties associated with the registration of optical and light detection and ranging (LIDAR) images. The proposed algorithm utilizes an exhaustive region correspondence search technique to determine the correspondence between regions of interest from the optical image with the LIDAR image over all translations for various rotations. The computational cost associated with exhaustive search is greatly reduced by exploiting the FFT. The substantial differences in intensity mappings between optical and LIDAR images are addressed through local feature mapping transformation optimization. Geometric distortions in the underlying images are dealt with through a geometric transformation estimation process that handles various transformations such as translation, rotation, scaling, shear, and perspective transformations. To account for mismatches caused by factors such as severe contrast differences, the proposed algorithm attempts to prune such outliers using the random sample consensus technique to improve registration accuracy. The proposed algorithm has been tested using various optical and LIDAR images and evaluated based on its registration accuracy. The results indicate that the proposed algorithm is suitable for the multimodal invariant registration of optical and LIDAR images.

}, keywords = {Fast Fourier Transform, fast Fourier transforms, feature mapping transformation optimization, FFT-accelerated approach, geometric distortions, geometric transformation estimation process, geophysical techniques, image registration, LIDAR images, light detection and ranging image registration, multimodal invariant registration, optical image registration, optical radar}, issn = {0196-2892}, doi = {http://dx.doi.org/10.1109/TGRS.2008.2001685}, author = {A Wong and J Orchard} } @inproceedings {449, title = {An advanced computational method to determine co-occurrence probability texture features}, booktitle = {IEEE International Geoscience and Remote Sensing Symposium}, year = {2002}, abstract = {

A critical shortcoming of determining co-occurrence probability texture features using Haralick\&$\#$39;s popular grey level co-occurrence matrix (GLCM) is the excessive computational burden. Here, a more robust algorithm (the grey level cooccurrence integrated algorithm or GLCIA) to perform this task is presented. The GLCIA is created by integrating the preferred aspects of two algorithms: the grey level cooccurrence hybrid structure (GLCHS) and the grey level cooccurrence hybrid histogram (GLCHH). The GLCHS utilizes a dedicated 2-d data structure to quickly generate the probabilities and apply statistics to generate the features. The GLCHH uses a more efficient 1-d data structure to perform the same tasks. Since the GLCHH is faster than the GLCHS yet the GLCHH is not able to calculate features using all available statistics, the integration of these two methods generates a superior algorithm (the GLCIA). The computational gains vary as a function of window size, quantization level, and statistics selected. The GLCIA computational time relative to that of the standard GLCM method ranges from 0.04\% to 16\%. The GLCIA is a highly recommended technique for anyone wishing to calculate co-occurrence probability texture features, especially from large-scale digital imagery.

}, keywords = {algorithm, co-occurrence probability, co-occurrence probability texture features, Feature Extraction, geophysical measurement technique, geophysical signal processing, geophysical techniques, GLCHH, GLCHS, GLCIA, grey level co occurrence hybrid histogram, grey level co occurrence hybrid structure, grey level co occurrence integrated algorithm, image texture, land surface, Remote Sensing, terrain mapping, texture feature}, doi = {http://dx.doi.org/10.1109/IGARSS.2002.1026575}, author = {D A. Clausi and Y Zhao} } @inproceedings {592, title = {An advanced computational method to determine co-occurrence probability texture features}, booktitle = {IEEE International Geoscience and Remote Sensing Symposium}, year = {2002}, abstract = {

A critical shortcoming of determining co-occurrence probability texture features using Haralick\&$\#$39;s popular grey level co-occurrence matrix (GLCM) is the excessive computational burden. Here, a more robust algorithm (the grey level cooccurrence integrated algorithm or GLCIA) to perform this task is presented. The GLCIA is created by integrating the preferred aspects of two algorithms: the grey level cooccurrence hybrid structure (GLCHS) and the grey level cooccurrence hybrid histogram (GLCHH). The GLCHS utilizes a dedicated 2-d data structure to quickly generate the probabilities and apply statistics to generate the features. The GLCHH uses a more efficient 1-d data structure to perform the same tasks. Since the GLCHH is faster than the GLCHS yet the GLCHH is not able to calculate features using all available statistics, the integration of these two methods generates a superior algorithm (the GLCIA). The computational gains vary as a function of window size, quantization level, and statistics selected. The GLCIA computational time relative to that of the standard GLCM method ranges from 0.04\% to 16\%. The GLCIA is a highly recommended technique for anyone wishing to calculate co-occurrence probability texture features, especially from large-scale digital imagery.

}, keywords = {algorithm, co-occurrence probability, co-occurrence probability texture features, Feature Extraction, geophysical measurement technique, geophysical signal processing, geophysical techniques, GLCHH, GLCHS, GLCIA, grey level co occurrence hybrid histogram, grey level co occurrence hybrid structure, grey level co occurrence integrated algorithm, image texture, land surface, Remote Sensing, terrain mapping, texture feature}, doi = {http://dx.doi.org/10.1109/IGARSS.2002.1026575}, author = {D A. Clausi and Y Zhao} } @inproceedings {593, title = {Automatic registration of SAR and visible band remote sensing images}, booktitle = {Joint IEEE International Geoscience and Remote Sensing Symposium and 24th Canadian Remote Sensing Symposium}, year = {2002}, abstract = {

Image registration is one of the basic image processing operations in remote sensing. With an increasing number of images collected every day from different sensors, automated registration of multi-sensor/multi-spectral images has become an important issue. A wide range of registration techniques exists for different types of applications and data sources, however no algorithm is known that can accurately register multi-source images consistently. This research addresses this problem by investigating the development of a fully automatic registration system for synthetic aperture radar (SAR) and optical remote sensing images. The development of this new automatic image registration method is based on the extraction and matching of common features that are visible in both images. The algorithm involves the following five steps: noise removal, edge extraction, edge linking pattern extraction and pattern matching. The application of the developed automatic image registration model to SAR and optical image pairs showed that accurate ground control points (GCPs) could be identified automatically.

}, keywords = {algorithm, edge extraction, edge linking pattern extraction, fully automatic registration, geophysical measurement technique, geophysical signal processing, geophysical techniques, image registration, land surface, multi-source image, noise removal, optical imaging, pattern matching, radar imaging, radar remote sensing, Remote Sensing, remote sensing by radar, remote sensing image, SAR, sensor fusion, synthetic aperture radar, terrain mapping, visible band}, doi = {http://dx.doi.org/10.1109/IGARSS.2002.1026106}, author = {M Ali and D A. Clausi} } @inproceedings {594, title = {Data fusion of SSM/I channels using multiresolution wavelet transform}, booktitle = {IEEE International Geoscience And Remote Sensing Symposium}, year = {2002}, abstract = {

This paper presents an approach to the fusion of SSM/I (Special Sensor Microwave/Imager) data from different resolutions, based on the prior statistical information about the data. The result is an estimated field that lives in a finer scale than any of the measurements. We apply a Wavelet Transform that increases speed and decreases memory requirements by sparsifying and preconditioning the statistics. This approach makes feasible the use of reprogrammable FPGA implementations for onboard satellite data processing, which greatly enhances flexibility and, most importantly, reduces communication burdens by limiting the extent to which raw, unprocessed data are transmitted to the ground.

}, keywords = {19 to 85.5 GHz, atmosphere, atmospheric techniques, EHF, geophysical measurement technique, geophysical signal processing, geophysical techniques, image fusion, land surface, meteorology, microwave radiometry, multiresolution wavelet transform, ocean, oceanographic techniques, preconditioning, prior statistical information, satellite remote sensing, sea surface, sensor fusion, SHF, sparsifying, Special Sensor Microwave/Imager, SSM/I, statistics, terrain mapping, wavelet transforms}, doi = {http://dx.doi.org/10.1109/IGARSS.2002.1026564}, author = {V K. Mehta and C M. Hammock and P Fieguth and H Krim} } @inproceedings {528, title = {Design and establishment of multi-scale spatial information system based on regional planning and decision making}, booktitle = {IEEE Geoscience and Remote Sensing Symposium}, year = {2001}, abstract = {

Fast development of Geomatics and network integrated technology demonstrates that spatial information are widely used in regional planning and decision making. Existing non-standardized multi-sources and multi-scales data have a shortage of spatial information sharing in both internal and external application departments or units, especially in the network era. A pilot regional planning and decision making spatial information system based on a standardized and network sharing technology is presented in this paper. The research area is composed of eight counties in Jinghong Region and Simao Region of Yunnan Province in China. Spatial information resources such as remote sensing, digital maps, and paper maps are collected from different departments. Statistics data such as agriculture, forest, earthquake hazard, ore, hydrogeology, ecology, transportation, population, tourism, and trade, etc. are the composition of the application system. The system will be mainly used by regional planners and managers in the province and the regions via Internet. The applications of general users are also considered. Geographic Metadata standardization for spatial information is used as an Internet engine between users and datasets. Arc/INFO and ArcView GIS are the basic software for information integration. WebGIS is used for spatial information sharing design and GeoMedia is accepted as a basic web software platform. Other software for multi-dimensional information display and management are designed and applied for virtual reality planning and application in the project

}, keywords = {agriculture, China, decision making, decision support systems, forest, geographic information system, geographic information systems, geomatics, geophysical measurement technique, geophysical signal processing, geophysical techniques, geophysics computing, GIS, Jinghong, land surface, land use, metadata, multi-dimensional information display, multiscale spatial information system, population, regional planning, Remote Sensing, sharing technology, Simao, tourism, town and country planning, vegetation, WebGIS, Yunnan Province}, doi = {http://dx.doi.org/10.1109/IGARSS.2001.977131}, author = {Y Zhao and D A. Clausi} } @inproceedings {566, title = {Rapid determination of co-occurrence texture features}, booktitle = {IEEE Geoscience and Remote Sensing Symposium}, year = {2001}, abstract = {

Typically, the co-occurrence features for image processing are calculated-by using a grey level co-occurrence matrix (GLCM). This method is computationally intensive since the matrix is usually sparse leading to many unnecessary calculations involving zero probabilities. An improvement on the GLCM method is to utilize a grey level co-occurrence linked list (GLCLL) to store only the non-zero co-occurring probabilities. The GLCLL suffers since, to achieve preferred computational speeds, the list should be sorted. This paper presents a grey level co-occurrence hybrid structure (GLCHS) based on an integrated hash table and linked list approach. Texture features obtained using this technique are identical to those obtained using the GLCM and GLCLL. Based on a Brodatz test image, the GLCHS method is demonstrated to be a superior technique when compared across various window sizes and grey level quantizations. The GLCHS method required, on average, 33.4\% of the computational time ( sigma;=3.08\%) required by the GLCLL. Significant computational gains are made using the GLCHS method

}, keywords = {Brodatz test image, co-occurrence features, co-occurrence texture feature, geophysical measurement technique, geophysical signal processing, geophysical techniques, grey level co-occurrence hybrid structure, grey level co-occurrence linked list, grey level co-occurrence matrix, image processing, image texture, integrated hash table, land surface, linked list, Remote Sensing, terrain mapping}, doi = {http://dx.doi.org/10.1109/IGARSS.2001.977103}, author = {D A. Clausi and Y Zhao} } @article {575, title = {The effect of speckle filtering on scale-dependent texture estimation of a forested scene}, journal = {IEEE Transactions on Geoscience and Remote Sensing}, volume = {38}, year = {2000}, pages = {1160 - 1170}, abstract = {

Spatial fluctuations in microwave backscatter may be an important piece of information in discriminating tree stands. However, the presence of speckle in synthetic aperture radar (SAR) image data is a barrier to the exploitation of image texture. The authors explored a new methodology that combines a recent adaptive speckle reduction algorithm by Lopes et al. (1990) with a generic texture estimation scheme. They investigated the claim that this filter was capable of preserving backscatter texture. To understand if speckle reduction was destroying backscatter texture, they compared the strength of the relationship between forest inventory parameters and image texture as a function of spatial scale for both filtered and unfiltered images. They used Radarsat Fine mode image data: single look resolution is approximately 8.5 m, and pixel spacing is 3 m. Their study area was northern Vancouver Island, B.C., on the west coast of Canada. For the unfiltered data, they found that the ability of image texture to predict the forest parameters decreased as the texture scale increased from 3 to 13 m, suggesting greater information content in the small scale texture. For the filtered data, this relationship was much weaker at small scales and was not a function of distance. Their results suggest that the speckle filter was not retaining small scale texture, which is consistent with the theoretical hypotheses underlying its multiplicative noise model. They also show that there is significant information in small state SAR image texture that may be used as an adjunct to other spatial information for discriminating tree stands in the temperate rain forest

}, keywords = {adaptive signal processing, adaptive speckle reduction algorithm, backscatter texture, forest, forested scene, forestry, generic texture estimation scheme, geophysical measurement technique, geophysical signal processing, geophysical techniques, image texture, land surface, microwave backscatter, radar imaging, radar remote sensing, remote sensing by radar, SAR, scale-dependent texture estimation, spatial fluctuations, speckle, speckle filtering, synthetic aperture radar, tree stand, vegetation mapping}, issn = {0196-2892}, author = {M J. Collins and J Wiebe and D A. Clausi} } @inproceedings {503, title = {Multiresolution Network Flow Phase Unwrapping}, booktitle = {IEEE 2000 International Geoscience and Remote Sensing Symposium}, year = {2000}, abstract = {

Phase unwrapping is a key step in deriving topographic information from interferometric SAR. Several recent methods are based on a minimum cost network flow approach. The authors develop a new, computationally efficient, ldquo;divide-and-conquer rdquo; strategy for phase unwrapping

}, keywords = {divide and conquer methods, divide-and-conquer method, geophysical measurement technique, geophysical techniques, InSAR, interferometric SAR, land surface topography, multiresolution network flow phase unwrapping, phase unwrapping, radar remote sensing, radar theory, remote sensing by radar, synthetic aperture radar, terrain mapping, topography (Earth)}, doi = {http://dx.doi.org/10.1109/IGARSS.2000.861693}, author = {G F Carballo and P Fieguth} } @inproceedings {513, title = {Probabilistic cost functions for network flow phase unwrapping}, booktitle = {IEEE International Geoscience and Remote Sensing Symposium}, year = {1999}, abstract = {

Phase unwrapping is a key step in deriving topographic information from interferometric SAR. A recent method is based on minimizing the total weighted length of branch cuts by computing the minimum cost flow on a network. The authors provide a theoretical foundation to derive the network weights based on a probabilistic approach

}, keywords = {branch cuts, geophysical measurement technique, geophysical techniques, InSAR, interferometric SAR, land surface topography, minimizing, minimum cost flow, network flow phase unwrapping, network weights, probabilistic approach, probabilistic cost function, radar imaging, radar remote sensing, radar theory, remote sensing by radar, SAR, synthetic aperture radar, terrain mapping, theoretical foundation, topographic mapping, topography (Earth), total weighted length}, doi = {http://dx.doi.org/10.1109/IGARSS.1999.772009}, author = {G F Carballo and P Fieguth} } @article {471, title = {A fast method to determine co-occurrence texture features}, journal = {IEEE Transactions on Geoscience and Remote Sensing}, volume = {36}, year = {1998}, pages = {298 - 300}, abstract = {

A critical shortcoming of determining texture features derived from grey-level co-occurrence matrices (GLCM\&$\#$39;s) is the excessive computational burden. This paper describes the implementation of a linked-list algorithm to determine co-occurrence texture features far more efficiently. Behavior of common co-occurrence texture features across difference grey-level quantizations is investigated

}, keywords = {co-occurrence texture feature, fast method, Feature Extraction, geophysical measurement technique, geophysical signal processing, geophysical techniques, grey-level co-occurrence matrices, grey-level quantization, image feature, image processing, image texture, land surface, linked-list algorithm, occurrence matrix, Remote Sensing, terrain mapping}, doi = {http://dx.doi.org/10.1109/36.655338}, author = {D A. Clausi and E Jernigan} }